DESCRIPTION (adapted from the Abstract): With success of the sequencing projects, the structural characterization of the genome-encoded proteins becomes increasingly important. Extensive knowledge of protein structure will significantly aid the investigation of protein function, protein interactions, and biochemical pathways. It will also have a major impact on our understanding of biology, human disease, and eventually on drug design. Experimental determination of structure is inherently time-consuming and costly, and currently there is a two orders-of- magnitude gap in numbers between sequencing and structure determination efforts. Computational techniques of structure modeling and prediction hold great promise for narrowing this gap. The Critical Assessment of Protein Structure Prediction (CASP) process was established to answer two questions: First, what level of prediction quality can be expected of these techniques? And second, which methods offer the best prospects for continued development? CASP is a community-wide program, with 98 research groups worldwide submitting over 3,800 predictions in the last round. Our group is the primary infrastructure resource for CASP, and handles processing of predictions, develops and implements evaluation software, performs prediction assessment, develops analysis and display tools, and facilitates access to predictions and their evaluation data. The primary goal of this project is to provide the infrastructure for the CASP prediction experiments. We propose to support the continuing operation of CASP and to expand its infrastructure, including an increased capacity for assessing predictions, further development and refinement of the evaluation criteria and software, and improved prediction analysis methods. We will also implement a continuous web-based mechanism for evaluation of prediction methods, based on structural genomics prediction targets. With this mechanism in place, we will be able to evaluate prediction methods on targets that are relevant to the interpretation of the genome sequence data, and record progress in structure prediction essentially in parallel with progress in structural genomics.